SOTAVerified

Self-Supervised Learning

Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration

Image source: LeCun

Papers

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Showing 2801–2850 of 5044 papers

TitleStatusHype
3D Pre-training improves GNNs for Molecular Property Prediction—0
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion—0
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement—0
3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization—0
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs—0
3-DUSSS: 3-Dimensional Ultrasonic Self Supervised Segmentation—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
AACP: Aesthetics assessment of children's paintings based on self-supervised learning—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Unifying Self-Supervised Clustering and Energy-Based Models—0
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects—0
Abnormality-Driven Representation Learning for Radiology Imaging—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
Accelerating Augmentation Invariance Pretraining—0
Accelerating Self-Supervised Learning via Efficient Training Strategies—0
Accurate and Robust Pulmonary Nodule Detection by 3D Feature Pyramid Network with Self-supervised Feature Learning—0
ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations—0
A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment—0
A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification—0
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup—0
A Collective Learning Framework to Boost GNN Expressiveness—0
A Comparative Study of Self-Supervised Speech Representations in Read and Spontaneous TTS—0
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023—0
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking—0
A Comprehensive Survey of Foundation Models in Medicine—0
A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More—0
A contrastive-learning approach for auditory attention detection—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning—0
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning—0
A Cookbook of Self-Supervised Learning—0
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History—0
Acoustic-to-articulatory inversion for dysarthric speech: Are pre-trained self-supervised representations favorable?—0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Action Shuffle Alternating Learning for Unsupervised Action Segmentation—0
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need—0
Active Semantic Localization with Graph Neural Embedding—0
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
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All datasetsDABSSTL-10CIFAR10cifar100ImageNet-100 (TEMI Split)TinyImageNetCIFAR-10CIFAR-100CREMA-DTiny ImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified